code: def _log_interpretation(self, out):
      # calculate interpretations etc for latter logging
      interpretation = self.interpret_output(
          detach(out),
          reduction="sum",
          attention_prediction_horizon=0,  # attention only for first prediction horizon
      )
      return interpretation
  
imports: from copy import copy
  from typing import Dict, List, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from torchmetrics import Metric
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss, QuantileLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nn import LSTM, MultiEmbedding
  from pytorch_forecasting.models.temporal_fusion_transformer.sub_modules import AddNorm, GateAddNorm, GatedLinearUnit, GatedResidualNetwork, InterpretableMultiHeadAttention, VariableSelectionNetwork
  from pytorch_forecasting.utils import create_mask, detach, integer_histogram, masked_op, padded_stack, to_list
label_desc: Use image node_id 8 for calling the TemporalFusionTransformer obj's underlying member method code with example usage: obj._log_interpretation(out) and returns: interpretation